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I am a senior research scientist at RISE Research institutes of Sweden heading The Deep Learning Research Group in Gothenburg. I have a PhD from Chalmers University of Technology, and I am the organizer of RISE Learning Machines Seminars.

I work on problems within applied AI where privacy, fairness, and efficiency is central. This includes work on federated learning, privacy-preserving representation learning, and generative adversarial netorks. The data modality varies, such as natural language, vision, and speech.

Some of our ongoing projects include The Federated Learning Testbed, The Swedish Medical Data Lab, AI Driven Financial Risk Assessment of Circular Business Models, and Smart Fire Detection.

Read more about me, or about my research group.


Federated learning using a mixture of experts

Arxiv 2020

Adversarial representation learning for private speech generation

ICML workshop on Self-supervision in Audio and Speech

Adversarial representation learning for synthetic replacement of private attributes

Arxiv 2020

Blood glucose prediction with variance estimation using recurrent neural networks


Semantic segmentation of fashion images using feature pyramid networks


Character-based recurrent neural networks for morphological relational reasoning

JLM 2019

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

CML 2016

Recent talks

Learned representations and what they encode


Social bias and fairness in NLP


Uncertainty in deep learning


Olof Mogren, PhD, RISE Research institutes of Sweden